Associations of socioeconomic status with transport-related physical activity: combining a household travel survey and accelerometer data using random forests

Background: Socioeconomic disparities in active transport have been documented in household travel surveys. However, active transport in these studies was operationalized with self-reported measures, which poorly approximate physical activity. Unfortunately, objective accelerometer data are very expensive to obtain in large-scale travel studies. Purpose: To benefit from a large sample and objective physical activity data, this study linked a cross-sectional household travel survey with accelerometer data from a small sample to investigate the association between socioeconomic disadvantage and the daily level of transport-related moderate-to vigorous physical activity (T-MVPA) in an adult population (35-83 years). Methods: Accelerometer data for participants' trips over 7 days from the RECORD GPS Study (7138 trips, 229 participants) were combined with information on participants' trips over 1 day from the Global Transport Survey (Enquete Globale Transport, EGT) (82084 trips, 21332 participants). Trip-level T-MVPA data from the RECORD sample were used to train a random forests prediction model, enabling the prediction of T-MVPA for each participant's trip from EGT. The associations between socioeconomic indicators and daily T-MVPA were analyzed with negative binomial regression models. Results: An average time of 18.9 min (95% confidence interval: 18.6-19.2) of T-MVPA was found for these 35-83 year old adults. The education level had a positive association with T-MVPA. Household income had a negative association with T-MVPA, especially for those people without a motorized vehicle. Conclusions: This study developed a methodology exporting precise sensor-based knowledge to a large survey sample to shed light on population-level socioeconomic disparities in transport-related physical activity. (C) 2016 Elsevier Ltd. All rights reserved.